TL;DR. OpenAlex is getting steadily better at linking research publications to the funders and grants behind them. But the methods that make that possible aren’t working as well for datasets. And we’re hearing from funders around the world that understanding the datasets they’re funding is critical to them. That gap is where DataCite’s funding metadata becomes essential, and OpenAlex now ingests it directly: funder and grant links on 716,297 dataset records. Put differently, DataCite supplies 96% of every dataset-funder link OpenAlex holds. DataCite’s work connecting funders and grants to research outputs is quietly becoming critical infrastructure for open funding intelligence.
We’re getting better at finding funders. Except for data.
We’re less than a year into our Wellcome-funded project to integrate research funding metadata into OpenAlex and we’re getting good at linking outputs to the underlying funders and grants. For journal articles, the share carrying both a funder and a specific grant number has climbed from about 6% of 2015 articles to roughly 15% of 2025 articles, which in absolute terms is more than 10 million articles published since 2015 linked to the grants that paid for them. Preprints and reviews are on the same upward path.
Then you get to datasets.
Figure 1. Share of each output type whose record carries both a funder and a grant number. Datasets, and software, sit near zero.
Just 1% of datasets published since 2015 carry a funder and grant, and unlike the article curve, that number is not climbing. These are not niche outputs. They are exactly the research products funders are asking us to help them track, and they are the ones where our coverage is thinnest.
A dataset has nowhere to say thank you
The reason is structural, and it is worth being precise about.
Most of what OpenAlex knows about who funded a piece of research, we learn from the paper itself (or directly from funders). Articles have an acknowledgements section, and it usually names the funder and the grant. We read those sections at scale, and it works: full-text mining has allowed us to assert millions of linkages between outputs and funders.
Datasets do not have acknowledgements sections. There is no paragraph at the end of a Zenodo deposit thanking the European Research Council. So the single most productive technique we have for articles produces almost nothing for data. It is not a tuning problem or a coverage problem. The text simply is not there to read. Researchers also seem to report datasets less frequently than research articles in their reports to funders (which we can also ingest).
That leaves exactly one place where the funding of a dataset can be recorded: the structured metadata supplied when the dataset is registered and gets its DOI. And that is precisely what DataCite built. Their metadata schema treats funding as a first-class field, fundingReferences, with room for the funder’s name, a persistent identifier for the funder, the award number, and the award title. It is a deliberate, unglamorous piece of infrastructure work, and it is the only reason the question in this post’s title has an answer at all.
Where dataset funding data actually comes from
We looked at every funder link on a dataset in OpenAlex and asked a simple question: which source told us about it?
Figure 2. Of the 716,297 funder-linked datasets, the share whose link each source supplied.
Of the 716,297 datasets in OpenAlex that carry a funder link*, DataCite asserted 685,778 of them, about 96%. Crossref accounts for around 12,000, and our own methods, the full-text mining and the funder grant records we ingest directly, for around 10,000 more.
This is not a story about one registration agency outperforming another. Crossref and DataCite serve different corners of the scholarly record, and each does the job its corner needs. The point is narrower and more interesting: for research outputs that are not articles, structured funding metadata at registration may be the only viable path forward.
One grant number, and everything it unlocks
Here is what makes this worth the effort.
The Mammal Diversity Database is deposited on Zenodo. Its DataCite registration declares one funder and one award number: the US National Science Foundation, grant 1441737. That is all the dataset itself says.
But OpenAlex already holds NSF grant 1441737, ingested directly from NSF’s own award data. So the two fuse, and the dataset inherits the entire grant record:
Collaborative Research: VertLife Terrestrial: A complete, global assembly of phylogenetic, trait, spatial and environment characteristics for a model clade
Principal investigator: Walter Jetz · $1,130,504 · 1 October 2014 to 30 September 2020
None of that was in the Zenodo record. All of it is now attached to the dataset, because a depositor typed a grant number into a form. This happens at scale: 190,361 datasets, about 28% of the ones in DataCite with funder information, connect to a full grant record from the funder’s own data this way– and that number will only grow as OpenAlex continues to ingest grant records directly from more funders.
The links also handle real-world complexity. A dataset on nuclear transport deposited in 4TU.ResearchData carries three grants from two funders: two from the Dutch Research Council, including “BaSyC – Building a Synthetic Cell,” and a European Research Council grant. The examples span every field and region we looked at, from an Australian Research Council grant behind a linguistics dataset on word order in Malayo-Polynesian languages, to a VINNOVA grant behind a Swedish mammography dataset built for AI research, to an ANR grant behind seismic data from a Réunion hotspot experiment.
The field is there but it is currently mostly empty.
Now the part we should all be less happy about.
The reason dataset coverage is only at 1% is not mainly that OpenAlex is missing links that exist. It is that the links were never registered in the first place. Even among the curated repositories where a researcher fills in a deposit form, the funder field usually stays blank: Zenodo sits around 8%, Figshare around 2%, Harvard Dataverse near zero.
The one clear outlier is the US Department of Energy’s OSTI, at 57%. Nearly all of that comes from national laboratories whose deposit workflows already know which DOE program paid for the work, so the field gets populated automatically. That is the whole lesson in one data point: when the deposit workflow captures the funder, the funder gets recorded. Where it is an optional free-text box at the bottom of a long form, it stays empty.
We can also see that when depositors do fill it in, they do it properly. Of the funder-linked datasets, 696,626, or 97%, carry a specific grant number and not just a funder name. People are not being vague. They are just not being asked.
What we built
As part of a project funded by Wellcome to build out open funding metadata, OpenAlex now ingests DataCite’s funding references directly. It went live on 18 June 2026 and refreshes every day, so new registrations flow through without waiting on a quarterly rebuild.
You can query it now. Every funder-linked dataset:
https://api.openalex.org/works?filter=type:dataset,funders.id:!null&include-xpac=true
Or everything a given funder has data-wise. Wellcome, for instance, has 2,432 datasets linked in OpenAlex today, and the NIH has 10,538:
https://api.openalex.org/works?filter=type:dataset,funders.id:F4320311904&include-xpac=true
All of it is in the API, the snapshot, and the web interface, under the same CC0 terms as everything else in OpenAlex.
What this means for you
If you run a repository, or publish datasets: add funder and award number to your deposit workflow and pass them to DataCite as fundingReferences. Even just the funder name and award number as text strings is enough. This is the single highest-leverage metadata change available to you right now, and the OSTI numbers show what happens when you make it.
If you are a funder with a data management policy: add a line to it. Ask your grantees to name the funder and the award number when they register a dataset with DataCite. That one sentence in your policy will do more for your ability to see and evidence your own data outputs than any amount of downstream text mining that we or anyone else can do, because for datasets that text mining structurally cannot work.
If you are a researcher depositing data: it is about thirty seconds at deposit time, and it is the difference between your dataset being connected to the work that paid for it and being an orphan.
Finally, our thanks to DataCite. Making funding a first-class field in the metadata schema, and doing the long, patient work of connecting funders and grants to research outputs, is exactly the kind of open infrastructure that is invisible when it works and impossible to substitute for when it is missing. As funders, institutions, and the public increasingly ask not just what research was published but what it produced and who paid for it, that connective tissue becomes the foundation of open funding intelligence. Everything in this post rests on it.
Spot something that looks wrong, or want to talk about connecting your funding data to OpenAlex? We would love to hear from you: support@openalex.org.
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* These figures leave out a small number of very large automated registrations, where one funder name is repeated across millions of machine-generated records. Because those reflect bulk data series rather than individual funding decisions and dwarf all other signals, we have set them aside for now, and will revisit how best to represent them.